A Fast Dynamic Evolutionary Multiobjective Algorithm via Manifold Transfer Learning

A Distributed Bi-behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization

“…Transfer-learning-based techniques are reliable alternatives for DMOPs based on the MOEA/D as a baseline system. In 2020, the new memory-driven manifold transfer learning was proposed based evolutionary algorithm (MMTL-MOEA/D) [51]. This approach has combined the memory mechanism to preserve the previous best solutions and the manifold transfer learning feature to estimate the best solutions, so that the best solutions are conserved and set as initial population of the next generation.…”

“…In addition, a randomly reinitialized mechanism (RI-MOEA/D) [51] is used to 10 % of selected populations after each change to maintain the diversity. A combination between the PPS [50] and the MOEA/D are considered in the PPS-MOEA/D algorithm to solve the DMOP.…”

“…DNSGA-II [1] Memory-based Approaches SGEA [9] dCOEA [49] Prediction-based methods PPS [50] Parallel Approaches MOEA/D [16] Transfer Learning-based Methods MMTL-MOEA/D [51].…”

“…The first comparative test is done for DMOPs using FDA, dMOP, UDF and F(ZJZ) benchmarks with 2 and 3 objectives. Five standard MOEAs [9] and the six-transfer learningbased methods [51] are compared to the new proposal DB-CSA system. All compared algorithms have the same parameters settings referring to the original publications [9] and [51].…”

“…Five standard MOEAs [9] and the six-transfer learningbased methods [51] are compared to the new proposal DB-CSA system. All compared algorithms have the same parameters settings referring to the original publications [9] and [51].…”